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      "gpuType": "V28"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "TPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CLygYGMFqfnh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import spacy\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "from sklearn.utils import resample\n",
        "import os\n",
        "os.environ['TF_USE_LEGACY_KERAS'] = '1'\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00O2Z3Yg8uYY"
      },
      "outputs": [],
      "source": [
        "#reading from imported csv's"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1d_UJ4zpI3QV",
        "outputId": "78e48146-79f4-4a56-92b3-381a2b84e033"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       word_count  statement_length  num_words  avg_word_length  \\\n",
              "0        2.397895          4.094345   2.397895         1.609438   \n",
              "1        5.556828          7.211557   5.556828         1.609438   \n",
              "2        4.615121          6.464588   4.615121         1.791759   \n",
              "3        3.610918          5.010635   3.610918         1.386294   \n",
              "4        4.521789          6.249975   4.521789         1.791759   \n",
              "...           ...               ...        ...              ...   \n",
              "41359    5.669881          7.329750   5.669881         1.609438   \n",
              "41360    5.273000          6.836259   5.273000         1.609438   \n",
              "41361    3.784190          5.375278   3.784190         1.609438   \n",
              "41362    5.533389          7.230563   5.533389         1.609438   \n",
              "41363    5.278115          6.957497   5.278115         1.609438   \n",
              "\n",
              "       vocabulary_size                                          statement  \\\n",
              "0             2.397895               caitlinaudrey aww suck go sydney one   \n",
              "1             5.176150  feel like kill still one listen convinc suffer...   \n",
              "2             4.382027  hellom hellodl hello know saydl okay someth mi...   \n",
              "3             3.295837  feel like believ realli go trial run see relie...   \n",
              "4             4.174387  depress realism exist matrix peopl came fine c...   \n",
              "...                ...                                                ...   \n",
              "41359         5.170484  feel need take reveng isol dont care sound edg...   \n",
              "41360         4.718499  went busi store today hello everyon hope your ...   \n",
              "41361         3.610918  even worth like worth live life full fear avoi...   \n",
              "41362         5.105945  cope gonna realli weird random want share beca...   \n",
              "41363         4.859812  friend first time long time cant leav went old...   \n",
              "\n",
              "       status  \n",
              "0           0  \n",
              "1           4  \n",
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              "3           1  \n",
              "4           1  \n",
              "...       ...  \n",
              "41359       6  \n",
              "41360       6  \n",
              "41361       6  \n",
              "41362       6  \n",
              "41363       6  \n",
              "\n",
              "[41364 rows x 7 columns]"
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              "      <th>4</th>\n",
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              "      <td>6.249975</td>\n",
              "      <td>4.521789</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>4.174387</td>\n",
              "      <td>depress realism exist matrix peopl came fine c...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>5.170484</td>\n",
              "      <td>feel need take reveng isol dont care sound edg...</td>\n",
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              "    <tr>\n",
              "      <th>41360</th>\n",
              "      <td>5.273000</td>\n",
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              "      <td>5.273000</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>4.718499</td>\n",
              "      <td>went busi store today hello everyon hope your ...</td>\n",
              "      <td>6</td>\n",
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              "      <th>41361</th>\n",
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              "      <td>3.610918</td>\n",
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              "      <td>5.105945</td>\n",
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              "      <td>4.859812</td>\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4ceea01f-0439-47c9-b989-12caf88d35ea button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_new');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_new",
              "summary": "{\n  \"name\": \"df_new\",\n  \"rows\": 41364,\n  \"fields\": [\n    {\n      \"column\": \"word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1781738782400697,\n        \"min\": 1.386294361,\n        \"max\": 7.505492275,\n        \"num_unique_values\": 837,\n        \"samples\": [\n          5.455321115,\n          7.051855623,\n          4.343805422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statement_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.18300247058754,\n        \"min\": 2.890371758,\n        \"max\": 9.167954655,\n        \"num_unique_values\": 2657,\n        \"samples\": [\n          7.60190196,\n          3.258096538,\n          7.33432935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1781738782400697,\n        \"min\": 1.386294361,\n        \"max\": 7.505492275,\n        \"num_unique_values\": 837,\n        \"samples\": [\n          5.455321115,\n          7.051855623,\n          4.343805422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_word_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10516913137838449,\n        \"min\": 1.386294361,\n        \"max\": 1.945910149,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.791759469,\n          1.945910149,\n          1.609437912\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vocabulary_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0056915396429034,\n        \"min\": 1.386294361,\n        \"max\": 6.519147288,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          5.707110265,\n          5.883322388,\n          5.433722004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18668,\n        \"samples\": [\n          \"basic get life togeth exam realli need motiv studi get decent grade howev today fight dad told loser degre chase useless want see anymor brought past feel sinc mani fight past last year relationship somewhat neutral everyth go downhil fall back yeah right loser disgrac tri year chang cannot anymor pain much think get drunk drive tree jump bridg fml good want die\",\n          \"marku ask date night two didnt hesit love man much think still date night made feel like young everyth use back dumb teenag happi could cri\",\n          \"battl depress whole life realiz even depress fight anymor realiz truli place world belong anywher love anyon anyth take space burden peopl think time place world\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df_new  =   pd.read_csv('/content/df_new_afterOutliers.csv',encoding = 'ISO-8859-1')\n",
        "df_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Eh-bctZ29CbY"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_new['statement'].astype(str), df_new['status'], test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VEYL14SKCHOd"
      },
      "outputs": [],
      "source": [
        "train_embeddings = pd.read_csv('/content/train_embeddings.csv',encoding = 'ISO-8859-1')\n",
        "test_embeddings = pd.read_csv('/content/test_embeddings.csv',encoding = 'ISO-8859-1')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BgBk12vb8asN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "SkhRkGzp1L9u"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install scikit-learn\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer()\n",
        "\n",
        "# Fit and transform the TF-IDF vectors\n",
        "vectorizer.fit(X_train)  # Fit to training data\n",
        "X_train_tfidf = vectorizer.transform(X_train)  # Transform training data\n",
        "X_test_tfidf = vectorizer.transform(X_test)    # Transform testing data\n",
        "\n",
        "# Concatenate BERT embeddings and TF-IDF features\n",
        "X_train_combined = np.hstack((train_embeddings, X_train_tfidf.toarray()))\n",
        "X_test_combined = np.hstack((test_embeddings, X_test_tfidf.toarray()))\n",
        "\n",
        "# 1. Normalize/Scale the Combined Features\n",
        "#scaler = StandardScaler()\n",
        "\n",
        "# Fit on the training data and transform both training and test data\n",
        "#X_train_combined_scaled = scaler.fit_transform(X_train_combined)\n",
        "#X_test_combined_scaled = scaler.transform(X_test_combined)\n",
        "\n"
      ],
      "metadata": {
        "id": "yiWWCWaA_KZQ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tfidf.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-I7vOQUG8-jz",
        "outputId": "c0a05352-8b5e-4f50-a2a8-abeee407174f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33091, 26894)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ivlzd_0v8-4b",
        "outputId": "b9a28965-bff9-42fa-8752-12e591e986af"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41364, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZRig5Wg9EPt",
        "outputId": "1b7675a6-802f-4cf2-9fa3-f4a3e529039b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8273, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKv9ZU-r9HiE",
        "outputId": "0c761b73-ada5-45fd-f6c1-ab1fcb1d9f7b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33091, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic regression"
      ],
      "metadata": {
        "id": "Yn-ilT_U8bCD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Initialize and train the Logistic Regression model without scaling\n",
        "logreg = LogisticRegression(max_iter=1000, random_state=42)\n",
        "logreg.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = logreg.predict(X_test_combined)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9g-Q0l818mJh",
        "outputId": "ef2b8982-ef72-4adb-febb-6882344cd61c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.89      0.90      1091\n",
            "           1       0.73      0.62      0.67      1221\n",
            "           2       0.73      0.74      0.73      1194\n",
            "           3       0.94      0.94      0.94      1227\n",
            "           4       0.95      0.97      0.96      1210\n",
            "           5       0.90      0.96      0.93      1156\n",
            "           6       0.96      1.00      0.98      1174\n",
            "\n",
            "    accuracy                           0.87      8273\n",
            "   macro avg       0.87      0.88      0.87      8273\n",
            "weighted avg       0.87      0.87      0.87      8273\n",
            "\n",
            "Confusion Matrix:\n",
            " [[ 976   18   26    9    7   44   11]\n",
            " [  40  762  290   40   32   40   17]\n",
            " [  52  230  882    6   11   11    2]\n",
            " [   2   15    6 1159    9   28    8]\n",
            " [   6    9    2    1 1178    5    9]\n",
            " [   2   14    5   24    2 1106    3]\n",
            " [   0    0    0    0    0    0 1174]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "\n",
        "# Binarize the labels for multi-class classification\n",
        "n_classes = len(np.unique(y_test))  # Number of classes\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "y_pred_prob = logreg.predict_proba(X_test_combined)  # Get probability estimates\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TmFpE2ixWdJR",
        "outputId": "d6297a98-85e1-4992-8d6b-74bd206f7e16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = logreg.predict(X_test_combined)\n",
        "y_train_pred = logreg.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: Logistic Regression\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZ87BBujIoWH",
        "outputId": "0fb72fe3-a9f3-4a23-ead3-ce7bb07d2260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Logistic Regression\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.8748\n",
            "Train Accuracy: 0.9168\n",
            "Test Precision (macro): 0.8718\n",
            "Train Precision (macro): 0.9156\n",
            "Test Recall (macro): 0.8760\n",
            "Train Recall (macro): 0.9177\n",
            "Test F1 Score (macro): 0.8731\n",
            "Train F1 Score (macro): 0.9159\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
        "import numpy as np\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = logreg.predict(X_test_combined)\n",
        "y_train_pred = logreg.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: Logistic Regression\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kp1EhA1JRnR",
        "outputId": "8754eded-9168-42da-b57e-dd6ea3da30d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 976   18   26    9    7   44   11]\n",
            " [  40  762  290   40   32   40   17]\n",
            " [  52  230  882    6   11   11    2]\n",
            " [   2   15    6 1159    9   28    8]\n",
            " [   6    9    2    1 1178    5    9]\n",
            " [   2   14    5   24    2 1106    3]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: Logistic Regression\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.8748\n",
            "Train Accuracy: 0.9168\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9054\n",
            "  Recall: 0.8946\n",
            "  F1 Score: 0.9000\n",
            "  Accuracy: 0.8946\n",
            "Class 1:\n",
            "  Precision: 0.7271\n",
            "  Recall: 0.6241\n",
            "  F1 Score: 0.6717\n",
            "  Accuracy: 0.6241\n",
            "Class 2:\n",
            "  Precision: 0.7283\n",
            "  Recall: 0.7387\n",
            "  F1 Score: 0.7335\n",
            "  Accuracy: 0.7387\n",
            "Class 3:\n",
            "  Precision: 0.9354\n",
            "  Recall: 0.9446\n",
            "  F1 Score: 0.9400\n",
            "  Accuracy: 0.9446\n",
            "Class 4:\n",
            "  Precision: 0.9508\n",
            "  Recall: 0.9736\n",
            "  F1 Score: 0.9620\n",
            "  Accuracy: 0.9736\n",
            "Class 5:\n",
            "  Precision: 0.8963\n",
            "  Recall: 0.9567\n",
            "  F1 Score: 0.9255\n",
            "  Accuracy: 0.9567\n",
            "Class 6:\n",
            "  Precision: 0.9592\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9791\n",
            "  Accuracy: 1.0000\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9218\n",
            "  Recall: 0.9305\n",
            "  F1 Score: 0.9261\n",
            "Class 1:\n",
            "  Precision: 0.8424\n",
            "  Recall: 0.7317\n",
            "  F1 Score: 0.7831\n",
            "Class 2:\n",
            "  Precision: 0.8296\n",
            "  Recall: 0.8432\n",
            "  F1 Score: 0.8364\n",
            "Class 3:\n",
            "  Precision: 0.9548\n",
            "  Recall: 0.9592\n",
            "  F1 Score: 0.9570\n",
            "Class 4:\n",
            "  Precision: 0.9678\n",
            "  Recall: 0.9804\n",
            "  F1 Score: 0.9741\n",
            "Class 5:\n",
            "  Precision: 0.9269\n",
            "  Recall: 0.9785\n",
            "  F1 Score: 0.9520\n",
            "Class 6:\n",
            "  Precision: 0.9656\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random Forest"
      ],
      "metadata": {
        "id": "ayQxQ6n28gvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "# Initialize and train the Random Forest model\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = rf_model.predict(X_test_combined)\n",
        "y_train_pred = rf_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: Random Forest\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n",
        "\n",
        "# Classification Report for both Test and Train sets\n",
        "print(\"\\nClassification Report (Test Set):\\n\", classification_report(y_test, y_test_pred))\n",
        "print(\"\\nClassification Report (Train Set):\\n\", classification_report(y_train, y_train_pred))\n"
      ],
      "metadata": {
        "id": "3mYNPdsjK4mF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Initialize and train the Random Forest model\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = rf_model.predict(X_test_combined)\n",
        "y_train_pred = rf_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: Random Forest\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score\n",
        "print(\"\\nTest Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in test_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQunJjKVK_5v",
        "outputId": "93075422-7a94-416c-de4e-8e8e4903d4dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 944   92   21    7    6   20    1]\n",
            " [ 137  829  213   14   20    8    0]\n",
            " [  98  270  815    6    4    1    0]\n",
            " [   5    6    0 1212    3    1    0]\n",
            " [   0    5    0    2 1203    0    0]\n",
            " [   0    2    0    0    0 1154    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: Random Forest\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.8861\n",
            "Train Accuracy: 0.9997\n",
            "\n",
            "Test Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.7973\n",
            "  Recall: 0.8653\n",
            "  F1 Score: 0.8299\n",
            "Class 1:\n",
            "  Precision: 0.6885\n",
            "  Recall: 0.6790\n",
            "  F1 Score: 0.6837\n",
            "Class 2:\n",
            "  Precision: 0.7769\n",
            "  Recall: 0.6826\n",
            "  F1 Score: 0.7267\n",
            "Class 3:\n",
            "  Precision: 0.9766\n",
            "  Recall: 0.9878\n",
            "  F1 Score: 0.9822\n",
            "Class 4:\n",
            "  Precision: 0.9733\n",
            "  Recall: 0.9942\n",
            "  F1 Score: 0.9836\n",
            "Class 5:\n",
            "  Precision: 0.9747\n",
            "  Recall: 0.9983\n",
            "  F1 Score: 0.9863\n",
            "Class 6:\n",
            "  Precision: 0.9991\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9996\n",
            "\n",
            "Train Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9995\n",
            "  Recall: 0.9998\n",
            "  F1 Score: 0.9996\n",
            "Class 1:\n",
            "  Precision: 0.9996\n",
            "  Recall: 0.9988\n",
            "  F1 Score: 0.9992\n",
            "Class 2:\n",
            "  Precision: 0.9998\n",
            "  Recall: 0.9996\n",
            "  F1 Score: 0.9997\n",
            "Class 3:\n",
            "  Precision: 0.9994\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9997\n",
            "Class 4:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n",
            "Class 5:\n",
            "  Precision: 0.9998\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9999\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (roc_curve, auc, precision_recall_curve,\n",
        "                             average_precision_score, classification_report,\n",
        "                             confusion_matrix, accuracy_score)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "\n",
        "# Train Random Forest model\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf.fit(X_train_combined, y_train)\n",
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = rf.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Random Forest - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Random Forest - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = rf.predict(X_test_combined)\n",
        "y_train_pred = rf.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: Random Forest Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OlrqajNaSvJL",
        "outputId": "9f3a2528-5956-4e74-eed7-c0a6ae220b81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 953   88   17    9    5   19    0]\n",
            " [ 137  811  224   16   23   10    0]\n",
            " [ 106  272  808    5    2    1    0]\n",
            " [   5    8    1 1211    0    2    0]\n",
            " [   0    2    0    0 1208    0    0]\n",
            " [   0    0    0    2    0 1154    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: Random Forest Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.8847\n",
            "Train Accuracy: 0.9997\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.7935\n",
            "  Recall: 0.8735\n",
            "  F1 Score: 0.8316\n",
            "  Accuracy: 0.8735\n",
            "Class 1:\n",
            "  Precision: 0.6867\n",
            "  Recall: 0.6642\n",
            "  F1 Score: 0.6753\n",
            "  Accuracy: 0.6642\n",
            "Class 2:\n",
            "  Precision: 0.7695\n",
            "  Recall: 0.6767\n",
            "  F1 Score: 0.7201\n",
            "  Accuracy: 0.6767\n",
            "Class 3:\n",
            "  Precision: 0.9743\n",
            "  Recall: 0.9870\n",
            "  F1 Score: 0.9806\n",
            "  Accuracy: 0.9870\n",
            "Class 4:\n",
            "  Precision: 0.9758\n",
            "  Recall: 0.9983\n",
            "  F1 Score: 0.9869\n",
            "  Accuracy: 0.9983\n",
            "Class 5:\n",
            "  Precision: 0.9730\n",
            "  Recall: 0.9983\n",
            "  F1 Score: 0.9855\n",
            "  Accuracy: 0.9983\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n",
            "  Accuracy: 1.0000\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9995\n",
            "  Recall: 0.9998\n",
            "  F1 Score: 0.9996\n",
            "Class 1:\n",
            "  Precision: 0.9996\n",
            "  Recall: 0.9988\n",
            "  F1 Score: 0.9992\n",
            "Class 2:\n",
            "  Precision: 0.9998\n",
            "  Recall: 0.9996\n",
            "  F1 Score: 0.9997\n",
            "Class 3:\n",
            "  Precision: 0.9994\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9997\n",
            "Class 4:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n",
            "Class 5:\n",
            "  Precision: 0.9998\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9999\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LightGBM"
      ],
      "metadata": {
        "id": "YuiwwDzm8kcq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lightgbm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Au6V9kP0LIHZ",
        "outputId": "d91cc967-57ab-49d5-907e-94dab75fa5e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lightgbm\n",
            "  Downloading lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl.metadata (17 kB)\n",
            "Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from lightgbm) (1.26.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from lightgbm) (1.13.1)\n",
            "Downloading lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl (3.6 MB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/3.6 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m2.6/3.6 MB\u001b[0m \u001b[31m37.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m35.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: lightgbm\n",
            "Successfully installed lightgbm-4.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "# Initialize and train the LightGBM model\n",
        "lgb_model = lgb.LGBMClassifier(random_state=42)\n",
        "lgb_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = lgb_model.predict(X_test_combined)\n",
        "y_train_pred = lgb_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: LightGBM\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n",
        "\n",
        "# Classification Report for both Test and Train sets\n",
        "print(\"\\nClassification Report (Test Set):\\n\", classification_report(y_test, y_test_pred))\n",
        "print(\"\\nClassification Report (Train Set):\\n\", classification_report(y_train, y_train_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJk6kqRGLJEx",
        "outputId": "926b9a7a-ca60-4794-9493-d5392da8a793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.482318 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 417549\n",
            "[LightGBM] [Info] Number of data points in the train set: 33091, number of used features: 5230\n",
            "[LightGBM] [Info] Start training from score -2.074708\n",
            "[LightGBM] [Info] Start training from score -1.917400\n",
            "[LightGBM] [Info] Start training from score -1.911660\n",
            "[LightGBM] [Info] Start training from score -1.935030\n",
            "[LightGBM] [Info] Start training from score -1.908802\n",
            "[LightGBM] [Info] Start training from score -1.940275\n",
            "[LightGBM] [Info] Start training from score -1.943224\n",
            "Model: LightGBM\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9150\n",
            "Train Accuracy: 0.9886\n",
            "Test Precision (macro): 0.9138\n",
            "Train Precision (macro): 0.9886\n",
            "Test Recall (macro): 0.9157\n",
            "Train Recall (macro): 0.9889\n",
            "Test F1 Score (macro): 0.9139\n",
            "Train F1 Score (macro): 0.9887\n",
            "\n",
            "Classification Report (Test Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.90      0.91      1091\n",
            "           1       0.81      0.69      0.74      1221\n",
            "           2       0.77      0.82      0.80      1194\n",
            "           3       0.97      1.00      0.98      1227\n",
            "           4       0.97      1.00      0.99      1210\n",
            "           5       0.96      1.00      0.98      1156\n",
            "           6       1.00      1.00      1.00      1174\n",
            "\n",
            "    accuracy                           0.92      8273\n",
            "   macro avg       0.91      0.92      0.91      8273\n",
            "weighted avg       0.91      0.92      0.91      8273\n",
            "\n",
            "\n",
            "Classification Report (Train Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      4156\n",
            "           1       0.98      0.95      0.97      4864\n",
            "           2       0.96      0.98      0.97      4892\n",
            "           3       1.00      1.00      1.00      4779\n",
            "           4       1.00      1.00      1.00      4906\n",
            "           5       1.00      1.00      1.00      4754\n",
            "           6       1.00      1.00      1.00      4740\n",
            "\n",
            "    accuracy                           0.99     33091\n",
            "   macro avg       0.99      0.99      0.99     33091\n",
            "weighted avg       0.99      0.99      0.99     33091\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Initialize and train the LightGBM model\n",
        "lgbm_model = LGBMClassifier(random_state=42)\n",
        "lgbm_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = lgbm_model.predict(X_test_combined)\n",
        "y_train_pred = lgbm_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: LightGBM\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score\n",
        "print(\"\\nTest Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in test_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgvTUL1aLNP5",
        "outputId": "4e3bc3f1-aec7-4cb7-82ea-2a183cb73a85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.451637 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 417549\n",
            "[LightGBM] [Info] Number of data points in the train set: 33091, number of used features: 5230\n",
            "[LightGBM] [Info] Start training from score -2.074708\n",
            "[LightGBM] [Info] Start training from score -1.917400\n",
            "[LightGBM] [Info] Start training from score -1.911660\n",
            "[LightGBM] [Info] Start training from score -1.935030\n",
            "[LightGBM] [Info] Start training from score -1.908802\n",
            "[LightGBM] [Info] Start training from score -1.940275\n",
            "[LightGBM] [Info] Start training from score -1.943224\n",
            "Confusion Matrix:\n",
            " [[ 986   38   20    4    5   38    0]\n",
            " [  41  842  269   26   24   14    5]\n",
            " [  46  158  985    1    2    2    0]\n",
            " [   1    3    0 1222    1    0    0]\n",
            " [   0    0    0    0 1210    0    0]\n",
            " [   0    3    0    2    0 1151    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: LightGBM\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9150\n",
            "Train Accuracy: 0.9886\n",
            "\n",
            "Test Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9181\n",
            "  Recall: 0.9038\n",
            "  F1 Score: 0.9109\n",
            "Class 1:\n",
            "  Precision: 0.8065\n",
            "  Recall: 0.6896\n",
            "  F1 Score: 0.7435\n",
            "Class 2:\n",
            "  Precision: 0.7732\n",
            "  Recall: 0.8250\n",
            "  F1 Score: 0.7982\n",
            "Class 3:\n",
            "  Precision: 0.9737\n",
            "  Recall: 0.9959\n",
            "  F1 Score: 0.9847\n",
            "Class 4:\n",
            "  Precision: 0.9742\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9869\n",
            "Class 5:\n",
            "  Precision: 0.9552\n",
            "  Recall: 0.9957\n",
            "  F1 Score: 0.9750\n",
            "Class 6:\n",
            "  Precision: 0.9958\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9979\n",
            "\n",
            "Train Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9804\n",
            "  Recall: 0.9988\n",
            "  F1 Score: 0.9895\n",
            "Class 1:\n",
            "  Precision: 0.9829\n",
            "  Recall: 0.9482\n",
            "  F1 Score: 0.9653\n",
            "Class 2:\n",
            "  Precision: 0.9600\n",
            "  Recall: 0.9759\n",
            "  F1 Score: 0.9679\n",
            "Class 3:\n",
            "  Precision: 0.9981\n",
            "  Recall: 0.9996\n",
            "  F1 Score: 0.9988\n",
            "Class 4:\n",
            "  Precision: 0.9998\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9999\n",
            "Class 5:\n",
            "  Precision: 0.9987\n",
            "  Recall: 0.9998\n",
            "  F1 Score: 0.9993\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import lightgbm as lgb\n",
        "from sklearn.metrics import (roc_curve, auc, precision_recall_curve,\n",
        "                             average_precision_score, classification_report,\n",
        "                             confusion_matrix, accuracy_score)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "\n",
        "# Train LightGBM model\n",
        "lgbm = lgb.LGBMClassifier(n_estimators=100, random_state=42)\n",
        "lgbm.fit(X_train_combined, y_train)\n",
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = lgbm.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('LightGBM - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('LightGBM - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = lgbm.predict(X_test_combined)\n",
        "y_train_pred = lgbm.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: LightGBM Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5e5cfc4LTXUk",
        "outputId": "c2b53820-9d37-4bab-a929-b26e18e38ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.648032 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 417549\n",
            "[LightGBM] [Info] Number of data points in the train set: 33091, number of used features: 5230\n",
            "[LightGBM] [Info] Start training from score -2.074708\n",
            "[LightGBM] [Info] Start training from score -1.917400\n",
            "[LightGBM] [Info] Start training from score -1.911660\n",
            "[LightGBM] [Info] Start training from score -1.935030\n",
            "[LightGBM] [Info] Start training from score -1.908802\n",
            "[LightGBM] [Info] Start training from score -1.940275\n",
            "[LightGBM] [Info] Start training from score -1.943224\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 986   38   20    4    5   38    0]\n",
            " [  41  842  269   26   24   14    5]\n",
            " [  46  158  985    1    2    2    0]\n",
            " [   1    3    0 1222    1    0    0]\n",
            " [   0    0    0    0 1210    0    0]\n",
            " [   0    3    0    2    0 1151    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: LightGBM Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9150\n",
            "Train Accuracy: 0.9886\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9181\n",
            "  Recall: 0.9038\n",
            "  F1 Score: 0.9109\n",
            "  Accuracy: 0.9038\n",
            "Class 1:\n",
            "  Precision: 0.8065\n",
            "  Recall: 0.6896\n",
            "  F1 Score: 0.7435\n",
            "  Accuracy: 0.6896\n",
            "Class 2:\n",
            "  Precision: 0.7732\n",
            "  Recall: 0.8250\n",
            "  F1 Score: 0.7982\n",
            "  Accuracy: 0.8250\n",
            "Class 3:\n",
            "  Precision: 0.9737\n",
            "  Recall: 0.9959\n",
            "  F1 Score: 0.9847\n",
            "  Accuracy: 0.9959\n",
            "Class 4:\n",
            "  Precision: 0.9742\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9869\n",
            "  Accuracy: 1.0000\n",
            "Class 5:\n",
            "  Precision: 0.9552\n",
            "  Recall: 0.9957\n",
            "  F1 Score: 0.9750\n",
            "  Accuracy: 0.9957\n",
            "Class 6:\n",
            "  Precision: 0.9958\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9979\n",
            "  Accuracy: 1.0000\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9804\n",
            "  Recall: 0.9988\n",
            "  F1 Score: 0.9895\n",
            "Class 1:\n",
            "  Precision: 0.9829\n",
            "  Recall: 0.9482\n",
            "  F1 Score: 0.9653\n",
            "Class 2:\n",
            "  Precision: 0.9600\n",
            "  Recall: 0.9759\n",
            "  F1 Score: 0.9679\n",
            "Class 3:\n",
            "  Precision: 0.9981\n",
            "  Recall: 0.9996\n",
            "  F1 Score: 0.9988\n",
            "Class 4:\n",
            "  Precision: 0.9998\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9999\n",
            "Class 5:\n",
            "  Precision: 0.9987\n",
            "  Recall: 0.9998\n",
            "  F1 Score: 0.9993\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Naive Bayes"
      ],
      "metadata": {
        "id": "hoTM8PRX8soP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "\n",
        "# Before fitting the model, clip negative values to 0\n",
        "X_train_combined_non_negative = np.clip(X_train_combined, 0, None)\n",
        "X_test_combined_non_negative = np.clip(X_test_combined, 0, None)\n",
        "\n",
        "# Initialize and train the Naive Bayes model using the modified data\n",
        "nb_model = MultinomialNB()\n",
        "nb_model.fit(X_train_combined_non_negative, y_train) # Use clipped data\n",
        "\n",
        "# Make predictions using the clipped test data\n",
        "y_test_pred = nb_model.predict(X_test_combined_non_negative)  # Use clipped data\n",
        "y_train_pred = nb_model.predict(X_train_combined_non_negative) # Use clipped data\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: Naive Bayes\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n",
        "\n",
        "# Classification Report for both Test and Train sets\n",
        "print(\"\\nClassification Report (Test Set):\\n\", classification_report(y_test, y_test_pred))\n",
        "print(\"\\nClassification Report (Train Set):\\n\", classification_report(y_train, y_train_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytJlEUnELYgL",
        "outputId": "abc56b66-dadc-44a9-fa74-98162357c2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Naive Bayes\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.5241\n",
            "Train Accuracy: 0.5316\n",
            "Test Precision (macro): 0.5852\n",
            "Train Precision (macro): 0.6005\n",
            "Test Recall (macro): 0.5290\n",
            "Train Recall (macro): 0.5382\n",
            "Test F1 Score (macro): 0.5247\n",
            "Train F1 Score (macro): 0.5344\n",
            "\n",
            "Classification Report (Test Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.48      0.83      0.61      1091\n",
            "           1       0.45      0.38      0.41      1221\n",
            "           2       0.58      0.30      0.39      1194\n",
            "           3       0.90      0.44      0.59      1227\n",
            "           4       0.80      0.56      0.66      1210\n",
            "           5       0.34      0.57      0.43      1156\n",
            "           6       0.56      0.62      0.59      1174\n",
            "\n",
            "    accuracy                           0.52      8273\n",
            "   macro avg       0.59      0.53      0.52      8273\n",
            "weighted avg       0.59      0.52      0.52      8273\n",
            "\n",
            "\n",
            "Classification Report (Train Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.83      0.59      4156\n",
            "           1       0.49      0.42      0.45      4864\n",
            "           2       0.63      0.32      0.42      4892\n",
            "           3       0.89      0.44      0.59      4779\n",
            "           4       0.83      0.54      0.66      4906\n",
            "           5       0.35      0.59      0.44      4754\n",
            "           6       0.55      0.63      0.59      4740\n",
            "\n",
            "    accuracy                           0.53     33091\n",
            "   macro avg       0.60      0.54      0.53     33091\n",
            "weighted avg       0.60      0.53      0.53     33091\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "\n",
        "# Before fitting the model, clip negative values to 0\n",
        "X_train_combined_non_negative = np.clip(X_train_combined, 0, None)\n",
        "X_test_combined_non_negative = np.clip(X_test_combined, 0, None)\n",
        "\n",
        "# Initialize and train the Naive Bayes model using the modified data\n",
        "nb_model = MultinomialNB()\n",
        "nb_model.fit(X_train_combined_non_negative, y_train) # Use clipped data\n",
        "\n",
        "# Make predictions using the clipped test data\n",
        "y_test_pred = nb_model.predict(X_test_combined_non_negative)  # Use clipped data\n",
        "y_train_pred = nb_model.predict(X_train_combined_non_negative) # Use clipped data\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: Naive Bayes\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score\n",
        "print(\"\\nTest Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in test_classification_report.keys():\n",
        "    if class_label not in ('accuracy', 'macro avg', 'weighted avg'):  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ('accuracy', 'macro avg', 'weighted avg'):  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "id": "Ve_KygXdLasc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0cc3aba-8c60-4b39-c16b-f5b055d604cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[904  13  49   7   8  80  30]\n",
            " [202 468 133  11  42 239 126]\n",
            " [222 364 353   4  10 183  58]\n",
            " [248  39   9 544  42 264  81]\n",
            " [100  46   9   5 674 260 116]\n",
            " [ 82 110  38  33  56 662 175]\n",
            " [134   8  18   0  12 271 731]]\n",
            "Model: Naive Bayes\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.5241\n",
            "Train Accuracy: 0.5316\n",
            "\n",
            "Test Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.4778\n",
            "  Recall: 0.8286\n",
            "  F1 Score: 0.6061\n",
            "Class 1:\n",
            "  Precision: 0.4466\n",
            "  Recall: 0.3833\n",
            "  F1 Score: 0.4125\n",
            "Class 2:\n",
            "  Precision: 0.5796\n",
            "  Recall: 0.2956\n",
            "  F1 Score: 0.3916\n",
            "Class 3:\n",
            "  Precision: 0.9007\n",
            "  Recall: 0.4434\n",
            "  F1 Score: 0.5942\n",
            "Class 4:\n",
            "  Precision: 0.7986\n",
            "  Recall: 0.5570\n",
            "  F1 Score: 0.6563\n",
            "Class 5:\n",
            "  Precision: 0.3379\n",
            "  Recall: 0.5727\n",
            "  F1 Score: 0.4250\n",
            "Class 6:\n",
            "  Precision: 0.5550\n",
            "  Recall: 0.6227\n",
            "  F1 Score: 0.5869\n",
            "\n",
            "Train Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.4596\n",
            "  Recall: 0.8263\n",
            "  F1 Score: 0.5907\n",
            "Class 1:\n",
            "  Precision: 0.4920\n",
            "  Recall: 0.4165\n",
            "  F1 Score: 0.4511\n",
            "Class 2:\n",
            "  Precision: 0.6295\n",
            "  Recall: 0.3205\n",
            "  F1 Score: 0.4248\n",
            "Class 3:\n",
            "  Precision: 0.8940\n",
            "  Recall: 0.4432\n",
            "  F1 Score: 0.5926\n",
            "Class 4:\n",
            "  Precision: 0.8281\n",
            "  Recall: 0.5422\n",
            "  F1 Score: 0.6553\n",
            "Class 5:\n",
            "  Precision: 0.3493\n",
            "  Recall: 0.5873\n",
            "  F1 Score: 0.4381\n",
            "Class 6:\n",
            "  Precision: 0.5507\n",
            "  Recall: 0.6316\n",
            "  F1 Score: 0.5884\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import (roc_curve, auc, precision_recall_curve,\n",
        "                             average_precision_score, classification_report,\n",
        "                             confusion_matrix, accuracy_score)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "\n",
        "# Train Naïve Bayes model\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train_combined, y_train)\n",
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = nb.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Naïve Bayes - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Naïve Bayes - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = nb.predict(X_test_combined)\n",
        "y_train_pred = nb.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: Naïve Bayes Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "npxrvoOYUh5b",
        "outputId": "1336570f-815d-4c9e-8bee-8c44f8d8f21d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 261   67   68   62   85  178  370]\n",
            " [  43  367  292   66  113  145  195]\n",
            " [  25  117  547   45  102  124  234]\n",
            " [   0    6    0  982   34  100  105]\n",
            " [   0    4    0    0 1107   48   51]\n",
            " [   0    0    0    0    2 1028  126]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: Naïve Bayes Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.6607\n",
            "Train Accuracy: 0.7763\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.7933\n",
            "  Recall: 0.2392\n",
            "  F1 Score: 0.3676\n",
            "  Accuracy: 0.2392\n",
            "Class 1:\n",
            "  Precision: 0.6542\n",
            "  Recall: 0.3006\n",
            "  F1 Score: 0.4119\n",
            "  Accuracy: 0.3006\n",
            "Class 2:\n",
            "  Precision: 0.6031\n",
            "  Recall: 0.4581\n",
            "  F1 Score: 0.5207\n",
            "  Accuracy: 0.4581\n",
            "Class 3:\n",
            "  Precision: 0.8502\n",
            "  Recall: 0.8003\n",
            "  F1 Score: 0.8245\n",
            "  Accuracy: 0.8003\n",
            "Class 4:\n",
            "  Precision: 0.7672\n",
            "  Recall: 0.9149\n",
            "  F1 Score: 0.8345\n",
            "  Accuracy: 0.9149\n",
            "Class 5:\n",
            "  Precision: 0.6334\n",
            "  Recall: 0.8893\n",
            "  F1 Score: 0.7398\n",
            "  Accuracy: 0.8893\n",
            "Class 6:\n",
            "  Precision: 0.5206\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.6847\n",
            "  Accuracy: 1.0000\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9479\n",
            "  Recall: 0.5825\n",
            "  F1 Score: 0.7216\n",
            "Class 1:\n",
            "  Precision: 1.0000\n",
            "  Recall: 0.5831\n",
            "  F1 Score: 0.7366\n",
            "Class 2:\n",
            "  Precision: 0.8833\n",
            "  Recall: 0.6233\n",
            "  F1 Score: 0.7308\n",
            "Class 3:\n",
            "  Precision: 0.9014\n",
            "  Recall: 0.8110\n",
            "  F1 Score: 0.8538\n",
            "Class 4:\n",
            "  Precision: 0.8626\n",
            "  Recall: 0.9060\n",
            "  F1 Score: 0.8838\n",
            "Class 5:\n",
            "  Precision: 0.6941\n",
            "  Recall: 0.9087\n",
            "  F1 Score: 0.7870\n",
            "Class 6:\n",
            "  Precision: 0.5530\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.7121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#AdaBoost"
      ],
      "metadata": {
        "id": "8i7WfBRo9ZMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "# Initialize and train the AdaBoost model\n",
        "ada_model = AdaBoostClassifier(random_state=42)\n",
        "ada_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = ada_model.predict(X_test_combined)\n",
        "y_train_pred = ada_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: AdaBoost\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n",
        "\n",
        "# Classification Report for both Test and Train sets\n",
        "print(\"\\nClassification Report (Test Set):\\n\", classification_report(y_test, y_test_pred))\n",
        "print(\"\\nClassification Report (Train Set):\\n\", classification_report(y_train, y_train_pred))\n"
      ],
      "metadata": {
        "id": "0zSkhP6FNPgh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a04a779-2f4d-4af2-8c16-b19e05269a20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: AdaBoost\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.6555\n",
            "Train Accuracy: 0.6476\n",
            "Test Precision (macro): 0.6578\n",
            "Train Precision (macro): 0.6517\n",
            "Test Recall (macro): 0.6578\n",
            "Train Recall (macro): 0.6519\n",
            "Test F1 Score (macro): 0.6546\n",
            "Train F1 Score (macro): 0.6480\n",
            "\n",
            "Classification Report (Test Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.85      0.72      1091\n",
            "           1       0.53      0.48      0.50      1221\n",
            "           2       0.61      0.55      0.58      1194\n",
            "           3       0.71      0.70      0.70      1227\n",
            "           4       0.81      0.75      0.78      1210\n",
            "           5       0.55      0.56      0.56      1156\n",
            "           6       0.78      0.72      0.75      1174\n",
            "\n",
            "    accuracy                           0.66      8273\n",
            "   macro avg       0.66      0.66      0.65      8273\n",
            "weighted avg       0.66      0.66      0.65      8273\n",
            "\n",
            "\n",
            "Classification Report (Train Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.86      0.71      4156\n",
            "           1       0.50      0.48      0.49      4864\n",
            "           2       0.61      0.56      0.58      4892\n",
            "           3       0.69      0.68      0.68      4779\n",
            "           4       0.81      0.73      0.77      4906\n",
            "           5       0.56      0.57      0.56      4754\n",
            "           6       0.78      0.70      0.74      4740\n",
            "\n",
            "    accuracy                           0.65     33091\n",
            "   macro avg       0.65      0.65      0.65     33091\n",
            "weighted avg       0.65      0.65      0.65     33091\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Initialize and train the AdaBoost model\n",
        "#ada_model = AdaBoostClassifier(random_state=42)\n",
        "#ada_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "#y_test_pred = ada_model.predict(X_test_combined)\n",
        "#y_train_pred = ada_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: AdaBoost\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score\n",
        "print(\"\\nTest Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in test_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "id": "VLXjj-9DNSl3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f059b44c-0c24-4d12-f133-3a9816730d47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[925  12  42   7   8  87  10]\n",
            " [127 587 277  58  48  84  40]\n",
            " [167 304 655   8  14  29  17]\n",
            " [ 63  43   6 853  65 160  37]\n",
            " [ 54  71  29  37 912  64  43]\n",
            " [112  52  48 163  42 651  88]\n",
            " [ 42  45  20  79  34 114 840]]\n",
            "Model: AdaBoost\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.6555\n",
            "Train Accuracy: 0.6476\n",
            "\n",
            "Test Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.6208\n",
            "  Recall: 0.8478\n",
            "  F1 Score: 0.7168\n",
            "Class 1:\n",
            "  Precision: 0.5269\n",
            "  Recall: 0.4808\n",
            "  F1 Score: 0.5028\n",
            "Class 2:\n",
            "  Precision: 0.6082\n",
            "  Recall: 0.5486\n",
            "  F1 Score: 0.5768\n",
            "Class 3:\n",
            "  Precision: 0.7079\n",
            "  Recall: 0.6952\n",
            "  F1 Score: 0.7015\n",
            "Class 4:\n",
            "  Precision: 0.8121\n",
            "  Recall: 0.7537\n",
            "  F1 Score: 0.7818\n",
            "Class 5:\n",
            "  Precision: 0.5475\n",
            "  Recall: 0.5631\n",
            "  F1 Score: 0.5552\n",
            "Class 6:\n",
            "  Precision: 0.7814\n",
            "  Recall: 0.7155\n",
            "  F1 Score: 0.7470\n",
            "\n",
            "Train Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.6092\n",
            "  Recall: 0.8568\n",
            "  F1 Score: 0.7121\n",
            "Class 1:\n",
            "  Precision: 0.4989\n",
            "  Recall: 0.4762\n",
            "  F1 Score: 0.4873\n",
            "Class 2:\n",
            "  Precision: 0.6124\n",
            "  Recall: 0.5556\n",
            "  F1 Score: 0.5826\n",
            "Class 3:\n",
            "  Precision: 0.6921\n",
            "  Recall: 0.6773\n",
            "  F1 Score: 0.6846\n",
            "Class 4:\n",
            "  Precision: 0.8118\n",
            "  Recall: 0.7299\n",
            "  F1 Score: 0.7687\n",
            "Class 5:\n",
            "  Precision: 0.5619\n",
            "  Recall: 0.5660\n",
            "  F1 Score: 0.5640\n",
            "Class 6:\n",
            "  Precision: 0.7755\n",
            "  Recall: 0.7017\n",
            "  F1 Score: 0.7367\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import (roc_curve, auc, precision_recall_curve,\n",
        "                             average_precision_score, classification_report,\n",
        "                             confusion_matrix, accuracy_score)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Train AdaBoost model\n",
        "adaboost = AdaBoostClassifier(\n",
        "    estimator=DecisionTreeClassifier(max_depth=1),  # Correct syntax\n",
        "    n_estimators=100,\n",
        "    learning_rate=1.0,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "adaboost.fit(X_train_combined, y_train)\n",
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = adaboost.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('AdaBoost - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('AdaBoost - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = adaboost.predict(X_test_combined)\n",
        "y_train_pred = adaboost.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: AdaBoost Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Cs738UWsU6-r",
        "outputId": "46f7fd8f-1e86-4d27-b6b8-59fb450214c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[898  19  26   7  14 117  10]\n",
            " [117 623 224  39  27 179  12]\n",
            " [190 363 513   0   7 116   5]\n",
            " [164  57   8 579  63 312  44]\n",
            " [ 50 104  19  26 792 177  42]\n",
            " [ 63  78  45 125  40 740  65]\n",
            " [ 73  62  28  31  48 234 698]]\n",
            "Model: AdaBoost Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.5854\n",
            "Train Accuracy: 0.5834\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.5775\n",
            "  Recall: 0.8231\n",
            "  F1 Score: 0.6788\n",
            "  Accuracy: 0.8231\n",
            "Class 1:\n",
            "  Precision: 0.4770\n",
            "  Recall: 0.5102\n",
            "  F1 Score: 0.4931\n",
            "  Accuracy: 0.5102\n",
            "Class 2:\n",
            "  Precision: 0.5944\n",
            "  Recall: 0.4296\n",
            "  F1 Score: 0.4988\n",
            "  Accuracy: 0.4296\n",
            "Class 3:\n",
            "  Precision: 0.7175\n",
            "  Recall: 0.4719\n",
            "  F1 Score: 0.5693\n",
            "  Accuracy: 0.4719\n",
            "Class 4:\n",
            "  Precision: 0.7992\n",
            "  Recall: 0.6545\n",
            "  F1 Score: 0.7197\n",
            "  Accuracy: 0.6545\n",
            "Class 5:\n",
            "  Precision: 0.3947\n",
            "  Recall: 0.6401\n",
            "  F1 Score: 0.4883\n",
            "  Accuracy: 0.6401\n",
            "Class 6:\n",
            "  Precision: 0.7968\n",
            "  Recall: 0.5945\n",
            "  F1 Score: 0.6810\n",
            "  Accuracy: 0.5945\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.5627\n",
            "  Recall: 0.8280\n",
            "  F1 Score: 0.6700\n",
            "Class 1:\n",
            "  Precision: 0.4850\n",
            "  Recall: 0.5343\n",
            "  F1 Score: 0.5085\n",
            "Class 2:\n",
            "  Precision: 0.6135\n",
            "  Recall: 0.4287\n",
            "  F1 Score: 0.5047\n",
            "Class 3:\n",
            "  Precision: 0.7059\n",
            "  Recall: 0.4742\n",
            "  F1 Score: 0.5673\n",
            "Class 4:\n",
            "  Precision: 0.7970\n",
            "  Recall: 0.6466\n",
            "  F1 Score: 0.7139\n",
            "Class 5:\n",
            "  Precision: 0.4036\n",
            "  Recall: 0.6310\n",
            "  F1 Score: 0.4923\n",
            "Class 6:\n",
            "  Precision: 0.7639\n",
            "  Recall: 0.5762\n",
            "  F1 Score: 0.6569\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#XGBoost"
      ],
      "metadata": {
        "id": "c7OxlUGnAN5w"
      }
    },
    {
      "source": [
        "!pip install xgboost"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBDHAgO9jMbW",
        "outputId": "355e7bb8-d569-4d31-f1ba-f4c56c7c2fdb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting xgboost\n",
            "  Downloading xgboost-2.1.4-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from xgboost) (1.26.4)\n",
            "Collecting nvidia-nccl-cu12 (from xgboost)\n",
            "  Downloading nvidia_nccl_cu12-2.25.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from xgboost) (1.13.1)\n",
            "Downloading xgboost-2.1.4-py3-none-manylinux_2_28_x86_64.whl (223.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m223.6/223.6 MB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nccl_cu12-2.25.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (201.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nccl-cu12, xgboost\n",
            "Successfully installed nvidia-nccl-cu12-2.25.1 xgboost-2.1.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "# Initialize and train the XGBoost model\n",
        "xgb_model = XGBClassifier(random_state=42, use_label_encoder=False, eval_metric='logloss')\n",
        "xgb_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = xgb_model.predict(X_test_combined)\n",
        "y_train_pred = xgb_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Precision (macro)\n",
        "test_precision_macro = precision_score(y_test, y_test_pred, average='macro')\n",
        "train_precision_macro = precision_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate Recall (macro)\n",
        "test_recall_macro = recall_score(y_test, y_test_pred, average='macro')\n",
        "train_recall_macro = recall_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Calculate F1 Score (macro)\n",
        "test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
        "train_f1_macro = f1_score(y_train, y_train_pred, average='macro')\n",
        "\n",
        "# Display the results\n",
        "print(\"Model: XGBoost\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Precision (macro): {test_precision_macro:.4f}\")\n",
        "print(f\"Train Precision (macro): {train_precision_macro:.4f}\")\n",
        "print(f\"Test Recall (macro): {test_recall_macro:.4f}\")\n",
        "print(f\"Train Recall (macro): {train_recall_macro:.4f}\")\n",
        "print(f\"Test F1 Score (macro): {test_f1_macro:.4f}\")\n",
        "print(f\"Train F1 Score (macro): {train_f1_macro:.4f}\")\n",
        "\n",
        "# Classification Report for both Test and Train sets\n",
        "print(\"\\nClassification Report (Test Set):\\n\", classification_report(y_test, y_test_pred))\n",
        "print(\"\\nClassification Report (Train Set):\\n\", classification_report(y_train, y_train_pred))\n"
      ],
      "metadata": {
        "id": "prnJvfV6NWS4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd0db8da-49f9-49b0-ee3b-111fc7dc2497"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: XGBoost\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9087\n",
            "Train Accuracy: 0.9807\n",
            "Test Precision (macro): 0.9066\n",
            "Train Precision (macro): 0.9807\n",
            "Test Recall (macro): 0.9095\n",
            "Train Recall (macro): 0.9812\n",
            "Test F1 Score (macro): 0.9072\n",
            "Train F1 Score (macro): 0.9808\n",
            "\n",
            "Classification Report (Test Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.91      0.90      1091\n",
            "           1       0.79      0.68      0.73      1221\n",
            "           2       0.77      0.80      0.78      1194\n",
            "           3       0.97      0.99      0.98      1227\n",
            "           4       0.97      1.00      0.98      1210\n",
            "           5       0.95      0.99      0.97      1156\n",
            "           6       1.00      1.00      1.00      1174\n",
            "\n",
            "    accuracy                           0.91      8273\n",
            "   macro avg       0.91      0.91      0.91      8273\n",
            "weighted avg       0.91      0.91      0.91      8273\n",
            "\n",
            "\n",
            "Classification Report (Train Set):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98      4156\n",
            "           1       0.97      0.92      0.94      4864\n",
            "           2       0.94      0.96      0.95      4892\n",
            "           3       1.00      1.00      1.00      4779\n",
            "           4       1.00      1.00      1.00      4906\n",
            "           5       1.00      1.00      1.00      4754\n",
            "           6       1.00      1.00      1.00      4740\n",
            "\n",
            "    accuracy                           0.98     33091\n",
            "   macro avg       0.98      0.98      0.98     33091\n",
            "weighted avg       0.98      0.98      0.98     33091\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Initialize and train the XGBoost model\n",
        "xgb_model = XGBClassifier(random_state=42, use_label_encoder=False, eval_metric='logloss')\n",
        "xgb_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = xgb_model.predict(X_test_combined)\n",
        "y_train_pred = xgb_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: XGBoost\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score\n",
        "print(\"\\nTest Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in test_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Precision, Recall, F1 Score for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label != 'accuracy' and class_label != 'macro avg' and class_label != 'weighted avg':  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "id": "kNtuIXheNegE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "980152da-656f-4f1b-f2be-259bec3dc37f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 988   32   20    7    8   35    1]\n",
            " [  46  827  271   25   28   21    3]\n",
            " [  66  174  950    1    1    2    0]\n",
            " [   0    4    0 1219    2    2    0]\n",
            " [   0    0    0    0 1210    0    0]\n",
            " [   0    4    0    2    0 1150    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: XGBoost\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9087\n",
            "Train Accuracy: 0.9807\n",
            "\n",
            "Test Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.8982\n",
            "  Recall: 0.9056\n",
            "  F1 Score: 0.9019\n",
            "Class 1:\n",
            "  Precision: 0.7944\n",
            "  Recall: 0.6773\n",
            "  F1 Score: 0.7312\n",
            "Class 2:\n",
            "  Precision: 0.7655\n",
            "  Recall: 0.7956\n",
            "  F1 Score: 0.7803\n",
            "Class 3:\n",
            "  Precision: 0.9721\n",
            "  Recall: 0.9935\n",
            "  F1 Score: 0.9827\n",
            "Class 4:\n",
            "  Precision: 0.9688\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9841\n",
            "Class 5:\n",
            "  Precision: 0.9504\n",
            "  Recall: 0.9948\n",
            "  F1 Score: 0.9721\n",
            "Class 6:\n",
            "  Precision: 0.9966\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9983\n",
            "\n",
            "Train Precision, Recall, F1 Score for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9643\n",
            "  Recall: 0.9957\n",
            "  F1 Score: 0.9798\n",
            "Class 1:\n",
            "  Precision: 0.9704\n",
            "  Recall: 0.9161\n",
            "  F1 Score: 0.9425\n",
            "Class 2:\n",
            "  Precision: 0.9365\n",
            "  Recall: 0.9579\n",
            "  F1 Score: 0.9470\n",
            "Class 3:\n",
            "  Precision: 0.9967\n",
            "  Recall: 0.9992\n",
            "  F1 Score: 0.9979\n",
            "Class 4:\n",
            "  Precision: 0.9996\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9998\n",
            "Class 5:\n",
            "  Precision: 0.9973\n",
            "  Recall: 0.9996\n",
            "  F1 Score: 0.9984\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import xgboost as xgb\n",
        "from sklearn.metrics import (roc_curve, auc, precision_recall_curve,\n",
        "                             average_precision_score, classification_report,\n",
        "                             confusion_matrix, accuracy_score)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "\n",
        "# Train XGBoost model\n",
        "xgb_model = xgb.XGBClassifier(\n",
        "    objective='multi:softprob',  # Multi-class classification\n",
        "    num_class=len(np.unique(y_train)),\n",
        "    eval_metric='mlogloss',\n",
        "    use_label_encoder=False,\n",
        "    n_estimators=100,\n",
        "    learning_rate=0.1,\n",
        "    max_depth=3,\n",
        "    random_state=42\n",
        ")\n",
        "xgb_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = xgb_model.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('XGBoost - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('XGBoost - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = xgb_model.predict(X_test_combined)\n",
        "y_train_pred = xgb_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: XGBoost Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SMsyTnV2Vb87",
        "outputId": "408b4c63-bbd5-498b-d391-9c098903bcd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[ 953   29   22    5    2   66   14]\n",
            " [  67  692  318   49   28   48   19]\n",
            " [ 131  206  820    6    6   22    3]\n",
            " [  25   28   12 1039   16   78   29]\n",
            " [  27   55   20   31  981   62   34]\n",
            " [  23   35   39   96    9  925   29]\n",
            " [  31   32   21   17    0   45 1028]]\n",
            "Model: XGBoost Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.7782\n",
            "Train Accuracy: 0.7815\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.7582\n",
            "  Recall: 0.8735\n",
            "  F1 Score: 0.8118\n",
            "  Accuracy: 0.8735\n",
            "Class 1:\n",
            "  Precision: 0.6425\n",
            "  Recall: 0.5667\n",
            "  F1 Score: 0.6023\n",
            "  Accuracy: 0.5667\n",
            "Class 2:\n",
            "  Precision: 0.6550\n",
            "  Recall: 0.6868\n",
            "  F1 Score: 0.6705\n",
            "  Accuracy: 0.6868\n",
            "Class 3:\n",
            "  Precision: 0.8359\n",
            "  Recall: 0.8468\n",
            "  F1 Score: 0.8413\n",
            "  Accuracy: 0.8468\n",
            "Class 4:\n",
            "  Precision: 0.9415\n",
            "  Recall: 0.8107\n",
            "  F1 Score: 0.8712\n",
            "  Accuracy: 0.8107\n",
            "Class 5:\n",
            "  Precision: 0.7424\n",
            "  Recall: 0.8002\n",
            "  F1 Score: 0.7702\n",
            "  Accuracy: 0.8002\n",
            "Class 6:\n",
            "  Precision: 0.8893\n",
            "  Recall: 0.8756\n",
            "  F1 Score: 0.8824\n",
            "  Accuracy: 0.8756\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.7526\n",
            "  Recall: 0.8847\n",
            "  F1 Score: 0.8133\n",
            "Class 1:\n",
            "  Precision: 0.6652\n",
            "  Recall: 0.5750\n",
            "  F1 Score: 0.6168\n",
            "Class 2:\n",
            "  Precision: 0.6676\n",
            "  Recall: 0.7165\n",
            "  F1 Score: 0.6912\n",
            "Class 3:\n",
            "  Precision: 0.8282\n",
            "  Recall: 0.8456\n",
            "  F1 Score: 0.8368\n",
            "Class 4:\n",
            "  Precision: 0.9422\n",
            "  Recall: 0.7905\n",
            "  F1 Score: 0.8597\n",
            "Class 5:\n",
            "  Precision: 0.7489\n",
            "  Recall: 0.8092\n",
            "  F1 Score: 0.7779\n",
            "Class 6:\n",
            "  Precision: 0.8915\n",
            "  Recall: 0.8686\n",
            "  F1 Score: 0.8799\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Proposed Model"
      ],
      "metadata": {
        "id": "lnvglJ_COWIG"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FYX_EtGXHKos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier, StackingClassifier\n",
        "\n",
        "# Define base models\n",
        "base_models = [\n",
        "    ('lgbm', lgb.LGBMClassifier(n_estimators=100, random_state=42)),\n",
        "    ('rf', RandomForestClassifier(n_estimators=100, random_state=42))\n",
        "]\n",
        "\n",
        "# Define XGBoost as final estimator\n",
        "final_estimator = XGBClassifier(\n",
        "    reg_alpha=0.5,  # Equivalent to alpha\n",
        "    reg_lambda=1.0,  # Equivalent to lambda_\n",
        "    learning_rate=0.05,\n",
        "    n_estimators=450,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Stacking classifier\n",
        "stacking_model = StackingClassifier(estimators=base_models, final_estimator=final_estimator)\n",
        "\n",
        "# Train the stacking model\n",
        "stacking_model.fit(X_train_combined, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = stacking_model.predict(X_test_combined)\n",
        "\n",
        "# Print accuracy\n",
        "print(f\"Ensemble Model Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiTrj5ludZUG",
        "outputId": "64ebba6a-f595-48ce-b897-6632d4a22fde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.607653 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 417549\n",
            "[LightGBM] [Info] Number of data points in the train set: 33091, number of used features: 5230\n",
            "[LightGBM] [Info] Start training from score -2.074708\n",
            "[LightGBM] [Info] Start training from score -1.917400\n",
            "[LightGBM] [Info] Start training from score -1.911660\n",
            "[LightGBM] [Info] Start training from score -1.935030\n",
            "[LightGBM] [Info] Start training from score -1.908802\n",
            "[LightGBM] [Info] Start training from score -1.940275\n",
            "[LightGBM] [Info] Start training from score -1.943224\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.497981 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 393896\n",
            "[LightGBM] [Info] Number of data points in the train set: 26472, number of used features: 4735\n",
            "[LightGBM] [Info] Start training from score -2.074919\n",
            "[LightGBM] [Info] Start training from score -1.917164\n",
            "[LightGBM] [Info] Start training from score -1.911783\n",
            "[LightGBM] [Info] Start training from score -1.935052\n",
            "[LightGBM] [Info] Start training from score -1.908721\n",
            "[LightGBM] [Info] Start training from score -1.940297\n",
            "[LightGBM] [Info] Start training from score -1.943194\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.481219 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 394044\n",
            "[LightGBM] [Info] Number of data points in the train set: 26473, number of used features: 4744\n",
            "[LightGBM] [Info] Start training from score -2.074656\n",
            "[LightGBM] [Info] Start training from score -1.917459\n",
            "[LightGBM] [Info] Start training from score -1.911565\n",
            "[LightGBM] [Info] Start training from score -1.935090\n",
            "[LightGBM] [Info] Start training from score -1.908759\n",
            "[LightGBM] [Info] Start training from score -1.940335\n",
            "[LightGBM] [Info] Start training from score -1.943232\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.487387 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 394473\n",
            "[LightGBM] [Info] Number of data points in the train set: 26473, number of used features: 4751\n",
            "[LightGBM] [Info] Start training from score -2.074656\n",
            "[LightGBM] [Info] Start training from score -1.917459\n",
            "[LightGBM] [Info] Start training from score -1.911565\n",
            "[LightGBM] [Info] Start training from score -1.935090\n",
            "[LightGBM] [Info] Start training from score -1.908759\n",
            "[LightGBM] [Info] Start training from score -1.940335\n",
            "[LightGBM] [Info] Start training from score -1.943232\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.462830 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 395223\n",
            "[LightGBM] [Info] Number of data points in the train set: 26473, number of used features: 4773\n",
            "[LightGBM] [Info] Start training from score -2.074656\n",
            "[LightGBM] [Info] Start training from score -1.917459\n",
            "[LightGBM] [Info] Start training from score -1.911565\n",
            "[LightGBM] [Info] Start training from score -1.935090\n",
            "[LightGBM] [Info] Start training from score -1.908759\n",
            "[LightGBM] [Info] Start training from score -1.940335\n",
            "[LightGBM] [Info] Start training from score -1.943232\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.488579 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 393698\n",
            "[LightGBM] [Info] Number of data points in the train set: 26473, number of used features: 4760\n",
            "[LightGBM] [Info] Start training from score -2.074656\n",
            "[LightGBM] [Info] Start training from score -1.917459\n",
            "[LightGBM] [Info] Start training from score -1.911821\n",
            "[LightGBM] [Info] Start training from score -1.934828\n",
            "[LightGBM] [Info] Start training from score -1.909014\n",
            "[LightGBM] [Info] Start training from score -1.940072\n",
            "[LightGBM] [Info] Start training from score -1.943232\n",
            "Ensemble Model Accuracy: 93.21%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Ensemble Model Accuracy: {accuracy_score(y_test, y_pred) * 100:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZP7N9MvIducJ",
        "outputId": "39dcb09b-86ca-4034-a911-c83130cf5a5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble Model Accuracy: 93.2068%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Get probability estimates for ROC & Precision-Recall curves\n",
        "y_pred_prob = stacking_model.predict_proba(X_test_combined)\n",
        "n_classes = len(np.unique(y_test))\n",
        "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
        "\n",
        "# Plot ROC Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Proposed Model - Multi-class ROC Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision-Recall Curves for each class\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(n_classes):\n",
        "    precision, recall, _ = precision_recall_curve(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    ap_score = average_precision_score(y_test_bin[:, i], y_pred_prob[:, i])\n",
        "    plt.plot(recall, precision, label=f'Class {i} (AP = {ap_score:.2f})')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Proposed Model - Multi-class Precision-Recall Curve')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Make predictions on test and train sets\n",
        "y_test_pred = stacking_model.predict(X_test_combined)\n",
        "y_train_pred = stacking_model.predict(X_train_combined)\n",
        "\n",
        "# Calculate Test and Train Accuracy\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "# Print confusion matrix to get class-specific details\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "\n",
        "# Classification report for each class\n",
        "test_classification_report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "train_classification_report = classification_report(y_train, y_train_pred, output_dict=True)\n",
        "\n",
        "# Calculate class-wise accuracy\n",
        "class_wise_accuracy = conf_matrix.diagonal() / conf_matrix.sum(axis=1)\n",
        "\n",
        "# Display the results for each class\n",
        "print(\"Model: stacking Classifier\")\n",
        "print(\"Disorder: (Specify disorder name if applicable)\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "\n",
        "# For each class, print Precision, Recall, F1 Score, and Accuracy\n",
        "print(\"\\nTest Metrics for each class:\")\n",
        "for i, class_label in enumerate(test_classification_report.keys()):\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {test_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {test_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {test_classification_report[class_label]['f1-score']:.4f}\")\n",
        "        print(f\"  Accuracy: {class_wise_accuracy[i]:.4f}\")\n",
        "\n",
        "print(\"\\nTrain Metrics for each class:\")\n",
        "for class_label in train_classification_report.keys():\n",
        "    if class_label not in ['accuracy', 'macro avg', 'weighted avg']:  # Ignore aggregated metrics\n",
        "        print(f\"Class {class_label}:\")\n",
        "        print(f\"  Precision: {train_classification_report[class_label]['precision']:.4f}\")\n",
        "        print(f\"  Recall: {train_classification_report[class_label]['recall']:.4f}\")\n",
        "        print(f\"  F1 Score: {train_classification_report[class_label]['f1-score']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2QhtAZNykF45",
        "outputId": "e2f57af6-d24f-4fc1-b149-310fdca95902"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[1036   36   16    2    0    1    0]\n",
            " [  53  966  195    5    2    0    0]\n",
            " [  40  197  957    0    0    0    0]\n",
            " [   2   11    0 1214    0    0    0]\n",
            " [   0    0    0    0 1210    0    0]\n",
            " [   0    2    0    0    0 1154    0]\n",
            " [   0    0    0    0    0    0 1174]]\n",
            "Model: stacking Classifier\n",
            "Disorder: (Specify disorder name if applicable)\n",
            "Test Accuracy: 0.9321\n",
            "Train Accuracy: 0.9984\n",
            "\n",
            "Test Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9160\n",
            "  Recall: 0.9496\n",
            "  F1 Score: 0.9325\n",
            "  Accuracy: 0.9496\n",
            "Class 1:\n",
            "  Precision: 0.7970\n",
            "  Recall: 0.7912\n",
            "  F1 Score: 0.7941\n",
            "  Accuracy: 0.7912\n",
            "Class 2:\n",
            "  Precision: 0.8193\n",
            "  Recall: 0.8015\n",
            "  F1 Score: 0.8103\n",
            "  Accuracy: 0.8015\n",
            "Class 3:\n",
            "  Precision: 0.9943\n",
            "  Recall: 0.9894\n",
            "  F1 Score: 0.9918\n",
            "  Accuracy: 0.9894\n",
            "Class 4:\n",
            "  Precision: 0.9983\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9992\n",
            "  Accuracy: 1.0000\n",
            "Class 5:\n",
            "  Precision: 0.9991\n",
            "  Recall: 0.9983\n",
            "  F1 Score: 0.9987\n",
            "  Accuracy: 0.9983\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n",
            "  Accuracy: 1.0000\n",
            "\n",
            "Train Metrics for each class:\n",
            "Class 0:\n",
            "  Precision: 0.9985\n",
            "  Recall: 0.9901\n",
            "  F1 Score: 0.9943\n",
            "Class 1:\n",
            "  Precision: 0.9971\n",
            "  Recall: 0.9981\n",
            "  F1 Score: 0.9976\n",
            "Class 2:\n",
            "  Precision: 0.9939\n",
            "  Recall: 0.9992\n",
            "  F1 Score: 0.9965\n",
            "Class 3:\n",
            "  Precision: 0.9994\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9997\n",
            "Class 4:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n",
            "Class 5:\n",
            "  Precision: 0.9998\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 0.9999\n",
            "Class 6:\n",
            "  Precision: 1.0000\n",
            "  Recall: 1.0000\n",
            "  F1 Score: 1.0000\n"
          ]
        }
      ]
    }
  ]
}